TY - GEN
T1 - Joint Holographic Detection and Reconstruction
AU - Yellin, Florence
AU - Béjar, Benjamín
AU - Haeffele, Benjamin D.
AU - Mathieu, Evelien
AU - Pick, Christian
AU - Ray, Stuart C.
AU - Vidal, René
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - Lens-free holographic imaging is important in many biomedical applications, as it offers a wider field of view, more mechanical robustness and lower cost than traditional microscopes. In many cases, it is important to be able to detect biological objects, such as blood cells, in microscopic images. However, state-of-the-art object detection methods are not designed to work on holographic images. Typically, the hologram must first be reconstructed into an image of the specimen, given a priori knowledge of the distance between the specimen and sensor, and standard object detection methods can then be used to detect objects in the reconstructed image. This paper describes a method for detecting objects directly in holograms while jointly reconstructing the image. This is achieved by assuming a sparse convolutional model for the objects being imaged and modeling the diffraction process responsible for generating the recorded hologram. This paper also describes an unsupervised method for training the convolutional templates, shows that the proposed method produces promising results for detecting white blood cells in holographic images, and demonstrates that the proposed object detection method is robust to errors in estimated focal depth.
AB - Lens-free holographic imaging is important in many biomedical applications, as it offers a wider field of view, more mechanical robustness and lower cost than traditional microscopes. In many cases, it is important to be able to detect biological objects, such as blood cells, in microscopic images. However, state-of-the-art object detection methods are not designed to work on holographic images. Typically, the hologram must first be reconstructed into an image of the specimen, given a priori knowledge of the distance between the specimen and sensor, and standard object detection methods can then be used to detect objects in the reconstructed image. This paper describes a method for detecting objects directly in holograms while jointly reconstructing the image. This is achieved by assuming a sparse convolutional model for the objects being imaged and modeling the diffraction process responsible for generating the recorded hologram. This paper also describes an unsupervised method for training the convolutional templates, shows that the proposed method produces promising results for detecting white blood cells in holographic images, and demonstrates that the proposed object detection method is robust to errors in estimated focal depth.
KW - Convolutional sparse coding
KW - Holography
KW - Phase recovery
UR - http://www.scopus.com/inward/record.url?scp=85075699240&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85075699240&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-32692-0_76
DO - 10.1007/978-3-030-32692-0_76
M3 - Conference contribution
AN - SCOPUS:85075699240
SN - 9783030326913
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 664
EP - 672
BT - Machine Learning in Medical Imaging - 10th International Workshop, MLMI 2019, Held in Conjunction with MICCAI 2019, Proceedings
A2 - Suk, Heung-Il
A2 - Liu, Mingxia
A2 - Lian, Chunfeng
A2 - Yan, Pingkun
PB - Springer
T2 - 10th International Workshop on Machine Learning in Medical Imaging, MLMI 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
Y2 - 13 October 2019 through 13 October 2019
ER -